Food trade disruption after global catastrophes

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Authors

Florian Ulrich Jehn, Łukasz G. Gajewski, Johanna Hedlund, Constantin W. Arnscheidt, Lili Xia, Nico Wunderling, David Denkenberger

Abstract

The global food trade system is resilient to minor disruptions but vulnerable to major ones. Major shocks can arise from global catastrophic risks, such as abrupt sunlight reduction scenarios (e.g., nuclear war) or global catastrophic infrastructure loss (e.g., due to severe geomagnetic storms or a global pandemic). We use a network model to examine how these two scenarios could impact global food trade, focusing on wheat, maize, soybeans, and rice, accounting for about 60% of global calorie intake. Our findings indicate that an abrupt sunlight reduction scenario, with soot emissions equivalent to a major nuclear war between India and Pakistan (37 Tg), could severely disrupt trade, causing most countries to lose the vast majority of their food imports (50-100 % decrease), primarily due to the main exporting countries being heavily affected. Global catastrophic infrastructure loss of the same magnitude as the abrupt sunlight reduction has a more homogeneous distribution of yield declines, resulting in most countries losing up to half of their food imports (25-50 % decrease). Thus, our analysis shows that both scenarios could significantly impact the food trade. However, the abrupt sunlight reduction scenario is likely more disruptive than global catastrophic infrastructure loss regarding the effects of yield reductions on food trade. This study underscores the vulnerabilities of the global food trade network to catastrophic risks and the need for enhanced preparedness.

DOI

https://doi.org/10.31223/X5MQ4R

Subjects

Agriculture, Human Geography, International and Area Studies, Nature and Society Relations, Other Geography

Keywords

Food Trade, Trade, agriculture, Food production, yield shock, Global Catastrophic Infrastructure Loss, Abrupt Sunlight Reduction Scenario, Geomagnetic Storm, Nuclear War, food security

Dates

Published: 2024-06-29 06:53

Last Updated: 2024-10-01 16:58

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License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
All data and code used for this study are available in the Github repository: https://github.com/allfed/pytradeshifts